An Efficient Way for Clustering Using Alternative Decision Tree
E. Gothai and P. Balasubramanie
DOI : 10.3844/ajassp.2012.531.534
American Journal of Applied Sciences
Volume 9, Issue 4
Problem statement: To Improve the quality of clustering; a Multi-Level Clustering (MLC) algorithm which produces a most accurate cluster with most closely related object using Alternative Decision Tree (ADT) technique is proposed. Approach: Our proposed method combines tree projection and condition for clustering formation and also is capable to produce a customizable cluster for varying kind of data along with varying number of cluster. Results: The experimental results shows that the proposed system has lower computational complexity, reduce time consumption; most optimize way for cluster formulation and clustering quality compared is compared effectively. Conclusion: The new method offers more accuracy of cluster data without manual intervention at the time of cluster formation. Compared to existing clustering algorithms either partition or hierarchical, our new method is more robust and easy to reach the solution of real world complex business problem.
© 2012 E. Gothai and P. Balasubramanie. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.